Work

Persistent entropy: a scale-invariant topological statistic for analyzing cell arrangements

Year: 2019

Type: article

Abstract: In this work, we develop a method for detecting diﬀerences in the topological distribution of cells forming epithelial tissues. In particular, we extract topological information from their images using persistent homology and a summary statistic called persistent entropy. This method is scale invariant, robust to noise and sensitive to global topological features of the tissue. We have found signiﬁcant diﬀerences between chick neuroepithelium and epithelium of Drosophila wing discs in both, larva and prepupal stages. Besides, we have tested our method, with good results, with images of mathematical tesselations that model biological tissues.

Source: LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)

Authors: María Nieves Atienza Martínez, Luis M. Escudero, María José Jiménez Rodríguez, Manuel Soriano Trigueros

Language: English

Topic: Topological and Geometric Data Analysis

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2913020470

FWCI: 1.284

Cites: 17

Cited by: 10

Related to: 10